The future of AI is getting a major boost from knowledge graph technology. Think of it like giving AI a dose of “common sense” through structured, up-to-date insights. Knowledge graphs can supercharge AI tools like Large Language Models, machine learning, and artificial neural networks (deep learning). So, knowledge graphs are a way to make AI smarter in context, which is a game changer for businesses, especially when optimizing supply chains. What’s more, it equips AI with factual-based “guardrails” to cut down on errors and hallucinations as well as makes its decisions more trustworthy. With knowledge graphs in the mix, AI becomes more accurate, transparent, and explainable.

In this article, I’ll first highlight the basic components of a knowledge graph and its capabilities. In fact, knowledge graphs are not new. They have already had a major effect on internet search methodologies and how personal assistants work such as Alexa and Siri. Next, I’ll explain how AI developers can leverage knowledge graphs to create contextual-based AI applications. Additionally, I’ll identify the new capabilities that contextual AI brings to businesses and supply chains.

“Deep learning presents entirely new opportunities for training neural commonsense models using a massive amount of raw text, fused with symbolic commonsense knowledge graphs.”

Yejin Choi

What Is a Knowledge Graph, and How Has This Technology Made the Internet Smarter?

A knowledge graph is a graphics-based data structure that brings a wealth of diverse information within an interconnected network. Specifically, these networks are where entities such as individuals, places, and objects are nodes linked by edges representing their mutual relationships. Indeed, knowledge graphs have already transformed search engines. Specifically, knowledge graphs provide search engines smarter information retrieval that goes beyond keyword matching to deliver precise, relevant content. See below, for more details on what a knowledge graph is and how it is having a major impact on internet search results and personal assistant chatbots.

1. So, What Is A Knowledge Graph Exactly?

Let’s first start off with a definition.

“Knowledge graphs are defined as graphs of data that accumulate and convey knowledge of the real world. The nodes in knowledge graphs represent the entities of interest, and the edges represent the relations between the entities.”

INDIAai

This definition aptly highlights that a knowledge graph is inherently dynamic rather than static. Knowledge graphs are characterized by their flexibility and robustness, which stem from two main features. Firstly, they are explicitly designed for continual knowledge accumulation. Secondly, they offer a wide application range by being constructed to impart knowledge rather than simply store data. At its core, a knowledge graph comprises three key elements. These include: 

The Components of a Knowledge Graph

• Nodes: These are real-world entities that can be objects, people, events, situations, or abstract concepts.
• Edges: These are the links that connect the nodes.
• Labels: These are the attributes that define the relationships between the nodes and reasoning rules on edges.

2. How Knowledge Graphs Have Made The Internet Smarter.

In particular, academic communities have used knowledge graphs for years, but it was the birth of the internet where this technology started to become very useful. This is because one of the main functions of the internet is information retrieval, and this is something that knowledge graph tech does well. With the introduction of the Google Knowledge Graph in 2012, both academic and business communities began to take great interest in this technology and its use began to expand.

AI Chatbot Technology – Its Staggering Benefits And How To Best Use

As the use of knowledge graph technology has expanded, it has become even more useful. For instance, the technology has increased in its capability to gather data from various data sources as well as improve the efficiency of storing graphs. Further, with the use of natural language processing (NLP) it has a better capability to describe and use context in retrieving results. This is evident where voice-based chatbots such as Amazon Alexa, Google Assistant, and Apple Siri leverage knowledge graphs to help answer questions. 

Additionally, we are seeing more uses of knowledge graphs in visual displays such as seen with Google search results panels. Here, the search results not only display links to web sites, but also detailed factual panels related to the search query. See below, for an example of a Google search result knowledge panel. Lastly, the use of knowledge graphs have expanded their use into business such as in the creation of Know Your Customer (KYC) guidelines. With this, more businesses are starting to use knowledge graphs in support of data analytics. In fact, Gartner expects that nearly 80 percent of all data and analytics innovation will include knowledge graphs by 2025.

For more discussion on knowledge graphs and its history, see Altexsoft’s Knowledge Graphs: The Essential Guide.

Moving Toward Knowledge Graph AI: 4 Ways A Knowledge Graph Can Help AI Be More Discerning, Contextual, and Trustworthy.

Data-Driven Decision-Making: Its Enormous Impact And The Truth On Limitations

Knowledge graphs are the perfect complement to AI’s Large Language Models (LLM). This is because knowledge graphs shore up LLM’s greatest weaknesses. Namely, knowledge graphs help AI to be more accurate, transparent, and explainable. For instance, AI can be less susceptible to hallucinations as knowledge graphs act as guardrails to help keep AI from providing answers that do not line up with the facts. Another thing knowledge graphs do for AI is help to explain its answers instead of just being a “black box”. This transparency and explainability helps us to better trust LLM responses. 

So, it is becoming apparent that we are moving toward a more knowledgeable AI. As a result, this provides incredible capabilities across all industries and use cases. To further detail, below are four ways knowledge graphs are helping AI to become more discerning and contextual.

1. Knowledge Graphs Can Train And Improve LLM Models.

Example: Medical Diagnoses.

Knowledge graphs serve as a structured and comprehensive foundation of real-world facts and relationships that can be used to train Large Language Models (LLMs). For example, let’s look at training a LLM app for medical diagnoses. First, AI developers can use a knowledge graph that contains an interconnected web of symptoms, diseases, medications, and patient histories. As a result, this allows the model to understand the complex relationships and nuances within medical data. So, this depth of understanding enables the AI to provide more accurate and contextually informed diagnoses. Hence, this reduces the likelihood of overlooking critical information.

2. LLMs Can Create Knowledge Graphs.

Example: Environmental Science

Also, LLMs have the capability to interpret and organize vast amounts of unstructured text into structured data, which can then be used to construct a knowledge graph. Take, for instance, the task of creating a knowledge graph for a specific field such as environmental science. A LLM can process thousands of scientific papers, extracting key concepts and their interrelations, such as pollution sources, ecosystem impacts, and conservation strategies. Thus, a LLM can effectively creat a knowledge graph that encapsulates the field’s collective understanding.

3. Knowledge Graphs On-Demand Can Enrich Both LLM’s Queries And Responses.

Examples: Financial Market Analysis

When knowledge graphs are continuously updated, they can significantly enhance LLMs. Specifically, knowledge graphs can provide AI apps the latest information to inform both the query prompts posed to the AI and the responses it generates. For instance, in the context of financial market analysis, a real-time knowledge graph that includes the latest stock prices, market trends, and news events can help an LLM to deliver more timely and relevant investment insights. As a result, the AI’s responses to queries about potential stock purchases are enriched with the most current data. As a result, this leads to better-informed decision-making for users.

To illustrate, let’s consider an investor who asks an AI app for help to understand what a merger between two tech firms could do. If the AI has current info from a knowledge graph, it can provide better answers with up-to-date market data, past examples of similar mergers, and insights from new articles. This way, when the AI answers the question about the merger, it doesn’t just give basic facts. Indeed, it goes deeper and explains what that merger could mean for the investor’s money.

4. Knowledge Graph AI: Addresses the Issues of Poor Data Quality and Empowers AI with Common Sense.

Examples: Supply Chains

Also, AI and knowledge graphs are a powerful combination, especially for supply chains. Indeed without knowledge graphs, supply chains have been slow to adopt AI and move beyond using AI as just another personal productivity tool or co-pilot. This is because supply chains are swamped with disjointed data across many data silos that are often incomplete, inaccurate, and out-of-date. This is also a major challenge with AI as it invariably struggles with poor data quality. For example, AI much too often provides unreliable and incoherent responses (e.g. hallucinations). Also, it often makes great recommendations without explaining how it arrived at them, causing doubt and delays verifying results.

Knowledge Graph AI: The Best Uses For Successful Supply Chains

Indeed, this is where knowledge graphs can address the issues of poor data quality, and empower AI with common sense. As a result, it makes AI a more reliable, intelligent tool for supply chain management. Hence, knowledge graphs and AI are a powerful combination that can tackle the toughest challenges in supply chains. Specifically, it bolsters AI to both better support decision-making and increases more opportunities for autonomous automation. For a more detailed discussion, see my article, Knowledge Graph AI: The Best Uses For Successful Supply Chains. This article includes 12 use cases across the supply chain from planning to procurement to supply chain operations to final delivery.

Also, for more discussion on how knowledge graphs are making AI smarter, see Jim Webber’s article, How knowledge graphs improve generative AI. Also, for more on AI limitations, see my article, AI Impact On Business Decisions – Know AI’s Unique Challenge To Overcome Its High Number Of Weaknesses.

What New Capabilities Does Contextual AI Offer Businesses?

Indeed, knowledge graphs make AI much more valuable to business operations and planning. AI to include LLMs using knowledge graphs now have new capabilities to be more than personal assistants or to augment internet searches. In fact, Knowledge Graph AI opens up a wide range of possibilities to businesses. To summarize, below are the powerful capabilities that knowledge graph tech brings to AI:

• Fact Verification. AI can deliver results that are fact checked.
• Fact Ranking. AI can prioritize results by ranking them against a knowledge graph.
• Related Entities. AI can provide more depth to its results as well as offer better contextual based results.  
• Entity Linking. LLM, AI agents, and users can better put things into context providing better results and references to authoritative content. 

For more discussion on the capabilities that knowledge graphs bring to AI, see GLASP’s The Power of Data, Compute, and Knowledge Graphs in the AI Era. Also, see WiseCube capabilities where they are leveraging knowledge graphs and Large Language Model (LLM) AI to offer research intelligence services for biomedical research organizations.

Conclusion.

So with the use of knowledge graphs, AI software such as Large Language Models (LLM), machine learning, and artificial neural networks (deep learning) are able to become more contextually aware. Indeed, this contextual-based AI is opening up unlimited possibilities for businesses, and in particular, supply chains. As a result, AI can now have factual-based “guardrails” in place to minimize hallucinations and enable us to better trust its results. As a result, AI coupled with knowledge graphs are more accurate, transparent, and explainable.

More References.

Below are additional references on the use of knowledge graphs and AI.

• Technical Overview of Knowledge Graphs: LeewayHertz’s UNDERSTANDING KNOWLEDGE GRAPHS: A KEY TO EFFECTIVE DATA GOVERNANCE.
• Overview of Knowledge Graph Benefits Using a Simple Retail Example: Semantic Art’s article, Data-Centric Approach to Managing Customer Data.
• LLMs and Knowledge Graphs: Daniel McCoy’s From Knowledge Graphs to Knowledge Flows.
• Scientific Review of AI, Intuitive Reasoning, and Knowledge: Yejin Choi’s paper, The Curious Case of Commonsense Intelligence.
• Achieving Data Centricity Leveraging Data-Intensive Tech like AI and Knowledge Graphs. See my article, Being A Data Centric Business: It’s Going Beyond The Frenzy Of More Big Apps And High Tech

Being A Data Centric Business: It’s Going Beyond The Frenzy Of More Big Apps And High Tech

For more from SC Tech Insights, see the latest articles on AI, Data, and Supply Chains.